test_mnist.py

from keras.datasets import mnist
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
#from sklearn.metrics import accuracy_score
import numpy as np
import matplotlib.pyplot as plt


(x_train, y_train), (x_test, y_test ) = mnist.load_data()

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format


input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format

x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded1 = MaxPooling2D((2, 2), padding='same')(x)

#model1 = Model(inputs=[input_img], outputs=[encoded1], name="encoder1")
#at this point the representation is (4, 4, 8) i.e. 128-dimensional

x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded1)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

model = Model(input_img, decoded, name='decoder')


model.compile(optimizer='adam',
          loss='mean_squared_error')

model.load_weights("model.h5")

ans_img = model.predict( x_test[0].reshape(1, 28, 28, 1) )
plt.figure(figsize=(28, 28))
plt.imshow(ans_img.reshape(28, 28))
plt.show()
f the dubious nature of our so-called "knowledge."